This paper presents an unsupervised segmentation method for feature sequences based on competitivelearning hidden Markov models. Models associated with the nodes of the Self-Organ...
Adaptor grammars (Johnson et al., 2007b) are a non-parametric Bayesian extension of Probabilistic Context-Free Grammars (PCFGs) which in effect learn the probabilities of entire s...
An enhanced self-organizing incremental neural network (ESOINN) is proposed to accomplish online unsupervised learning tasks. It improves the self-organizing incremental neural ne...
In this paper a methodology for feature selection in unsupervised learning is proposed. It makes use of a multiobjective genetic algorithm where the minimization of the number of ...
This paper presents an unsupervised learning approach to disambiguate various relations between name entities by use of various lexical and syntactic features from the contexts. I...
Jinxiu Chen, Dong-Hong Ji, Chew Lim Tan, Zheng-Yu ...